adding back tf code + adding models comparison on SQuAD

tensorflow_code/optimization.py

@@ -0,0 +1,171 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Functions and classes related to optimization (weight updates)."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import re
+import tensorflow as tf
+
+
+def create_optimizer(loss, init_lr, num_train_steps, num_warmup_steps, use_tpu):
+    """Creates an optimizer training op."""
+    global_step = tf.train.get_or_create_global_step()
+
+    learning_rate = tf.constant(value=init_lr, shape=[], dtype=tf.float32)
+
+    # Implements linear decay of the learning rate.
+    learning_rate = tf.train.polynomial_decay(
+        learning_rate,
+        global_step,
+        num_train_steps,
+        end_learning_rate=0.0,
+        power=1.0,
+        cycle=False)
+
+    # Implements linear warmup. I.e., if global_step < num_warmup_steps, the
+    # learning rate will be `global_step/num_warmup_steps * init_lr`.
+    if num_warmup_steps:
+        global_steps_int = tf.cast(global_step, tf.int32)
+        warmup_steps_int = tf.constant(num_warmup_steps, dtype=tf.int32)
+
+        global_steps_float = tf.cast(global_steps_int, tf.float32)
+        warmup_steps_float = tf.cast(warmup_steps_int, tf.float32)
+
+        warmup_percent_done = global_steps_float / warmup_steps_float
+        warmup_learning_rate = init_lr * warmup_percent_done
+
+        is_warmup = tf.cast(global_steps_int < warmup_steps_int, tf.float32)
+        learning_rate = (
+                (1.0 - is_warmup) * learning_rate + is_warmup * warmup_learning_rate)
+
+    # It is recommended that you use this optimizer for fine tuning, since this
+    # is how the model was trained (note that the Adam m/v variables are NOT
+    # loaded from init_checkpoint.)
+    optimizer = AdamWeightDecayOptimizer(
+        learning_rate=learning_rate,
+        weight_decay_rate=0.01,
+        beta_1=0.9,
+        beta_2=0.999,
+        epsilon=1e-6,
+        exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"])
+
+    if use_tpu:
+        optimizer = tf.contrib.tpu.CrossShardOptimizer(optimizer)
+
+    tvars = tf.trainable_variables()
+    grads = tf.gradients(loss, tvars)
+
+    # This is how the model was pre-trained.
+    (grads, _) = tf.clip_by_global_norm(grads, clip_norm=1.0)
+
+    train_op = optimizer.apply_gradients(
+        zip(grads, tvars), global_step=global_step)
+
+    new_global_step = global_step + 1
+    train_op = tf.group(train_op, [global_step.assign(new_global_step)])
+    return train_op
+
+
+class AdamWeightDecayOptimizer(tf.train.Optimizer):
+    """A basic Adam optimizer that includes "correct" L2 weight decay."""
+
+    def __init__(self,
+                 learning_rate,
+                 weight_decay_rate=0.0,
+                 beta_1=0.9,
+                 beta_2=0.999,
+                 epsilon=1e-6,
+                 exclude_from_weight_decay=None,
+                 name="AdamWeightDecayOptimizer"):
+        """Constructs a AdamWeightDecayOptimizer."""
+        super(AdamWeightDecayOptimizer, self).__init__(False, name)
+
+        self.learning_rate = learning_rate
+        self.weight_decay_rate = weight_decay_rate
+        self.beta_1 = beta_1
+        self.beta_2 = beta_2
+        self.epsilon = epsilon
+        self.exclude_from_weight_decay = exclude_from_weight_decay
+
+    def apply_gradients(self, grads_and_vars, global_step=None, name=None):
+        """See base class."""
+        assignments = []
+        for (grad, param) in grads_and_vars:
+            if grad is None or param is None:
+                continue
+
+            param_name = self._get_variable_name(param.name)
+
+            m = tf.get_variable(
+                name=param_name + "/adam_m",
+                shape=param.shape.as_list(),
+                dtype=tf.float32,
+                trainable=False,
+                initializer=tf.zeros_initializer())
+            v = tf.get_variable(
+                name=param_name + "/adam_v",
+                shape=param.shape.as_list(),
+                dtype=tf.float32,
+                trainable=False,
+                initializer=tf.zeros_initializer())
+
+            # Standard Adam update.
+            next_m = (
+                    tf.multiply(self.beta_1, m) + tf.multiply(1.0 - self.beta_1, grad))
+            next_v = (
+                    tf.multiply(self.beta_2, v) + tf.multiply(1.0 - self.beta_2,
+                                                              tf.square(grad)))
+
+            update = next_m / (tf.sqrt(next_v) + self.epsilon)
+
+            # Just adding the square of the weights to the loss function is *not*
+            # the correct way of using L2 regularization/weight decay with Adam,
+            # since that will interact with the m and v parameters in strange ways.
+            #
+            # Instead we want ot decay the weights in a manner that doesn't interact
+            # with the m/v parameters. This is equivalent to adding the square
+            # of the weights to the loss with plain (non-momentum) SGD.
+            if self._do_use_weight_decay(param_name):
+                update += self.weight_decay_rate * param
+
+            update_with_lr = self.learning_rate * update
+
+            next_param = param - update_with_lr
+
+            assignments.extend(
+                [param.assign(next_param),
+                 m.assign(next_m),
+                 v.assign(next_v)])
+        return tf.group(*assignments, name=name)
+
+    def _do_use_weight_decay(self, param_name):
+        """Whether to use L2 weight decay for `param_name`."""
+        if not self.weight_decay_rate:
+            return False
+        if self.exclude_from_weight_decay:
+            for r in self.exclude_from_weight_decay:
+                if re.search(r, param_name) is not None:
+                    return False
+        return True
+
+    def _get_variable_name(self, param_name):
+        """Get the variable name from the tensor name."""
+        m = re.match("^(.*):\\d+$", param_name)
+        if m is not None:
+            param_name = m.group(1)
+        return param_name